Task Success Metrics

What counts as "success" varies by task. A research agent needs accuracy and completeness, while a customer service agent prioritizes issue resolution and satisfaction. Always define success in terms of the specific task requirements.

Don't track metrics just because they're easy to measure. Focus on metrics that directly relate to user value and business goals. A high response time means nothing if outputs are incorrect.

Before setting targets, measure current performance to establish a baseline. This shows where you are now and helps set realistic improvement goals. You can't improve what you don't measure.

Distinguish between must-have metrics (accuracy, task completion) and nice-to-have metrics (response time, resource usage). Don't sacrifice primary metrics to optimize secondary ones.

Combine automated testing, human evaluation, user feedback, and production monitoring. Each method reveals different aspects of performance. Automated tests are fast but limited; human evaluation catches nuanced quality issues.

Research what successful agents in your domain achieve. If the industry standard is 90% accuracy and you're at 70%, you know improvement is needed. Benchmarks provide context for your metrics.

Metrics are only useful if they inform decisions. If success rate drops, investigate why and fix it. If accuracy is below target, improve training data or prompts. Metrics without action are just numbers.

Going from 60% to 95% success doesn't happen overnight. Set realistic interim targets: first 70%, then 80%, then 90%. Celebrate progress at each milestone to maintain momentum.

Performance can degrade over time due to input drift, API changes, or edge cases. Set up continuous monitoring in production to catch regressions early before they impact many users.

Use metrics to guide improvement efforts. If error analysis shows most failures are in a specific category, focus there first. Let data drive priorities rather than guessing what needs work.